In the past ten years, the market of portable devices (for example, smart phones, tablets, and smart watches) has been developed at an ultra-high velocity. The portable equipment has been widely applied to human life, for example, short message, phone, navigation, game, website browsing, social media, etc. Currently, most of the portable devices have powerful processors, wireless transceivers, Global Navigation Satellite System (GNSS) receivers and many sensors. Due to their popularization and powerful hardware, these portable devices have become ideal platforms for navigation-related applications.
The GNSS receiver embedded in the portable device can provide an accurate positioning solution in an open sky environment, but it cannot work well in a severe environment such as urban canyons and indoors. In the challenging environments, other wireless techniques (for example, WiFi and BLUETOOTH® wireless technology standard) are usually used to replace or assist the GNSS receivers to provide the positioning solution. In these wireless positioning techniques, WiFi positioning only adopts the existing infrastructure, while BLUETOOTH® wireless technology standard positioning generally deploys BLUETOOTH® wireless technology standard Low Energy (BLE) beacons. These wireless positioning systems usually estimate the location of a target by various different, measurements, for example, Time Of Arrival (TOA), Time Difference Of Arrival (TDOA), Angle of Arrival (AOA) and Received Signal Strength (RSS). The wireless positioning system using TOA, TDOA and AOA is complex and expensive and has poor performance in a dynamic indoor environment. These systems also need special hardware, which makes them cannot be supported by the current mainstream portable devices. On the contrary, the RSS-based method has been widely applied to the indoor wireless positioning system and has been supported by the current portable devices.
RSS-based wireless positioning techniques can be classified into two categories: trilateration and fingerprinting (FP). Trilateration needs to know the position of the wireless access points (APs) as a priori information. Then, the RSS value is transformed into the distance between the target and the wireless AP through a wireless propagation model. Finally, the position of the target is estimated by adopting the least squares (LSQ) to the AP positions and the distances (between the target and the APs). On the other hand, the wireless positioning system based on fingerprinting includes two phases: wireless fingerprint database construction and real-time positioning. The wireless fingerprint database construction phase generates wireless fingerprints by measuring the position information and the corresponding RSS values at these measurement points, and stores these fingerprints to construct the wireless signal map database. The real-time positioning phase uses different methods to match the current RSS values with the fingerprints in the wireless signal map database to determine the position of the target. Generally, wireless fingerprinting provides a more accurate positioning solution than trilateration. However, it needs more labor and time costs to construct the database. In order to realize an accurate positioning solution, wireless fingerprinting is adopted in this patent.
The fingerprinting-based wireless positioning system usually has the following limitations: 1) it cannot provide complete navigation solution (three-dimensional position, velocity and attitude); 2) the system performance highly depends on APs, for example distribution and available quantity of APs; 3) almost none of these systems can provide smooth and continuous positioning solution with high sampling rate; 4) the wireless RSS value often fluctuates in a severe environment, which is usually caused by reflection, attenuation and shadowing. It is difficult for the wireless positioning standalone system based on fingerprinting to eliminate these limitations.
The relative navigation technique based on micro-electromechanical system (MEMS) sensors can potentially reduce limitations of these wireless positioning systems. The MEMS navigation system can provide a complete smooth navigation solution with high sampling rate. In addition, the MEMS navigation system is self-contained systems which cannot be affected by environments. Therefore, these MEMS navigation systems can further be used to fill in the positioning gap in a sparsely distributed wireless AP environment. However, due to noises of the MEMS sensors in the portable devices, these MEMS standalone navigation solution can be accurate only for a short term. Therefore, these MEMS navigation systems need wireless signals to limit their drifting. Due to the complementary property of the wireless fingerprinting and MEMS sensors, they are integrated to realize a more accurate and stable navigation solution.
Most of current existing integration methods use an integration filter to fuse the wireless fingerprint solution and MEMS navigation solution, the fusion result is only fed back to the MEMS sensor to limit the drifting. However, the fusion result is not fed back to wireless fingerprinting to improve its performance.